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<p>A layer of Gaussian neurons.  
 <a href="classshark_1_1_gaussian_layer.html#details">More...</a></p>

<p><code>#include &lt;<a class="el" href="_gaussian_layer_8h_source.html">shark/Unsupervised/RBM/Neuronlayers/GaussianLayer.h</a>&gt;</code></p>
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  <img id="dynsection-0-trigger" src="closed.png" alt="+"/> Inheritance diagram for shark::GaussianLayer:</div>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a id="pub-types" name="pub-types"></a>
Public Types</h2></td></tr>
<tr class="memitem:a346b4d4edf91fa583578207488fc7fca" id="r_a346b4d4edf91fa583578207488fc7fca"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="structshark_1_1_real_space.html">RealSpace</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a346b4d4edf91fa583578207488fc7fca">StateSpace</a></td></tr>
<tr class="memdesc:a346b4d4edf91fa583578207488fc7fca"><td class="mdescLeft">&#160;</td><td class="mdescRight">the bias terms associated with the neurons  <br /></td></tr>
<tr class="separator:a346b4d4edf91fa583578207488fc7fca"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4c79a6809fa54a8788fc9a73f9661759" id="r_a4c79a6809fa54a8788fc9a73f9661759"><td class="memItemLeft" align="right" valign="top">typedef RealVector&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a4c79a6809fa54a8788fc9a73f9661759">SufficientStatistics</a></td></tr>
<tr class="memdesc:a4c79a6809fa54a8788fc9a73f9661759"><td class="mdescLeft">&#160;</td><td class="mdescRight">The sufficient statistics for the Guassian Layer stores the mean of the neuron and the inverse temperature.  <br /></td></tr>
<tr class="separator:a4c79a6809fa54a8788fc9a73f9661759"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a510c15554b7b4bebec1c966e7ccaff95" id="r_a510c15554b7b4bebec1c966e7ccaff95"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="structshark_1_1_batch.html">Batch</a>&lt; <a class="el" href="classshark_1_1_gaussian_layer.html#a4c79a6809fa54a8788fc9a73f9661759">SufficientStatistics</a> &gt;::type&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">StatisticsBatch</a></td></tr>
<tr class="memdesc:a510c15554b7b4bebec1c966e7ccaff95"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sufficient statistics of a batch of data.  <br /></td></tr>
<tr class="separator:a510c15554b7b4bebec1c966e7ccaff95"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_types_classshark_1_1_i_parameterizable"><td colspan="2" onclick="javascript:toggleInherit('pub_types_classshark_1_1_i_parameterizable')"><img src="closed.png" alt="-"/>&#160;Public Types inherited from <a class="el" href="classshark_1_1_i_parameterizable.html">shark::IParameterizable&lt; VectorType &gt;</a></td></tr>
<tr class="memitem:a2ad5e2e60b2b352988b41f46024d790b inherit pub_types_classshark_1_1_i_parameterizable" id="r_a2ad5e2e60b2b352988b41f46024d790b"><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="_c_svm_linear_8cpp.html#ab106d665148183a2dc94dcf8716c9203">VectorType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_i_parameterizable.html#a2ad5e2e60b2b352988b41f46024d790b">ParameterVectorType</a></td></tr>
<tr class="separator:a2ad5e2e60b2b352988b41f46024d790b inherit pub_types_classshark_1_1_i_parameterizable"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a id="pub-methods" name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a877687dcf1eb19c17c0a2c941e7bf0a5" id="r_a877687dcf1eb19c17c0a2c941e7bf0a5"><td class="memItemLeft" align="right" valign="top">const RealVector &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a877687dcf1eb19c17c0a2c941e7bf0a5">bias</a> () const</td></tr>
<tr class="memdesc:a877687dcf1eb19c17c0a2c941e7bf0a5"><td class="mdescLeft">&#160;</td><td class="mdescRight">Returns the bias values of the units.  <br /></td></tr>
<tr class="separator:a877687dcf1eb19c17c0a2c941e7bf0a5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aaf729abb5e4ab69322d57f2a1b5fe39b" id="r_aaf729abb5e4ab69322d57f2a1b5fe39b"><td class="memItemLeft" align="right" valign="top">RealVector &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#aaf729abb5e4ab69322d57f2a1b5fe39b">bias</a> ()</td></tr>
<tr class="memdesc:aaf729abb5e4ab69322d57f2a1b5fe39b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Returns the bias values of the units.  <br /></td></tr>
<tr class="separator:aaf729abb5e4ab69322d57f2a1b5fe39b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad0d61deb12bd4455dbf6546d08328b72" id="r_ad0d61deb12bd4455dbf6546d08328b72"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#ad0d61deb12bd4455dbf6546d08328b72">resize</a> (std::size_t newSize)</td></tr>
<tr class="memdesc:ad0d61deb12bd4455dbf6546d08328b72"><td class="mdescLeft">&#160;</td><td class="mdescRight">Resizes this neuron layer.  <br /></td></tr>
<tr class="separator:ad0d61deb12bd4455dbf6546d08328b72"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad2a3749d604f6e425f069726cae281d1" id="r_ad2a3749d604f6e425f069726cae281d1"><td class="memItemLeft" align="right" valign="top">std::size_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">size</a> () const</td></tr>
<tr class="memdesc:ad2a3749d604f6e425f069726cae281d1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Returns the number of neurons of this layer.  <br /></td></tr>
<tr class="separator:ad2a3749d604f6e425f069726cae281d1"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0e768ac30ba61b25220fa17293591d0c" id="r_a0e768ac30ba61b25220fa17293591d0c"><td class="memTemplParams" colspan="2">template&lt;class Input , class BetaVector &gt; </td></tr>
<tr class="memitem:a0e768ac30ba61b25220fa17293591d0c"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a0e768ac30ba61b25220fa17293591d0c">sufficientStatistics</a> (Input const &amp;input, <a class="el" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">StatisticsBatch</a> &amp;statistics, BetaVector const &amp;beta) const</td></tr>
<tr class="memdesc:a0e768ac30ba61b25220fa17293591d0c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Takes the input of the neuron and estimates the expectation of the response of the neuron.  <br /></td></tr>
<tr class="separator:a0e768ac30ba61b25220fa17293591d0c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a11e7d48c8d1b8faf811d3870d9ce818a" id="r_a11e7d48c8d1b8faf811d3870d9ce818a"><td class="memTemplParams" colspan="2">template&lt;class Matrix , class Rng &gt; </td></tr>
<tr class="memitem:a11e7d48c8d1b8faf811d3870d9ce818a"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a11e7d48c8d1b8faf811d3870d9ce818a">sample</a> (<a class="el" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">StatisticsBatch</a> const &amp;statistics, Matrix &amp;state, double alpha, Rng &amp;rng) const</td></tr>
<tr class="memdesc:a11e7d48c8d1b8faf811d3870d9ce818a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Given a the precomputed statistics (the mean of the Gaussian), the elements of the vector are sampled. This happens either with Gibbs-Sampling or Flip-the-State sampling. For alpha= 0 gibbs sampling is performed. That is the next state for neuron i is directly taken from the conditional distribution of the i-th neuron. In the case of alpha=1, flip-the-state sampling is performed, which takes the last state into account and tries to do deterministically jump into states with higher probability. THIS IS NOT IMPLEMENTED YET and alpha is ignored!  <br /></td></tr>
<tr class="separator:a11e7d48c8d1b8faf811d3870d9ce818a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a117e4b765db5d8b37c2446ae6451937e" id="r_a117e4b765db5d8b37c2446ae6451937e"><td class="memTemplParams" colspan="2">template&lt;class Matrix &gt; </td></tr>
<tr class="memitem:a117e4b765db5d8b37c2446ae6451937e"><td class="memTemplItemLeft" align="right" valign="top">RealVector&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a117e4b765db5d8b37c2446ae6451937e">logProbability</a> (<a class="el" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">StatisticsBatch</a> const &amp;statistics, Matrix const &amp;state) const</td></tr>
<tr class="memdesc:a117e4b765db5d8b37c2446ae6451937e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes the log of the probability of the given states in the conditional distribution.  <br /></td></tr>
<tr class="separator:a117e4b765db5d8b37c2446ae6451937e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab7e9bdbf26ccea6190311b81ee4741cb" id="r_ab7e9bdbf26ccea6190311b81ee4741cb"><td class="memTemplParams" colspan="2">template&lt;class Matrix &gt; </td></tr>
<tr class="memitem:ab7e9bdbf26ccea6190311b81ee4741cb"><td class="memTemplItemLeft" align="right" valign="top">Matrix const &amp;&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#ab7e9bdbf26ccea6190311b81ee4741cb">phi</a> (Matrix const &amp;state) const</td></tr>
<tr class="memdesc:ab7e9bdbf26ccea6190311b81ee4741cb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Transforms the current state of the neurons for the multiplication with the weight matrix of the <a class="el" href="classshark_1_1_r_b_m.html" title="stub for the RBM class. at the moment it is just a holder of the parameter set and the Energy.">RBM</a>, i.e. calculates the value of the phi-function used in the interaction term. In the case of Gaussian neurons the phi-function is just the identity.  <br /></td></tr>
<tr class="separator:ab7e9bdbf26ccea6190311b81ee4741cb"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9d8d02a951be66e02b06e21c9b646ab8" id="r_a9d8d02a951be66e02b06e21c9b646ab8"><td class="memItemLeft" align="right" valign="top">RealMatrix const &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a9d8d02a951be66e02b06e21c9b646ab8">expectedPhiValue</a> (<a class="el" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">StatisticsBatch</a> const &amp;statistics) const</td></tr>
<tr class="memdesc:a9d8d02a951be66e02b06e21c9b646ab8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Returns the expectation of the phi-function.  <br /></td></tr>
<tr class="separator:a9d8d02a951be66e02b06e21c9b646ab8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad8dfcc9b50d9bae82eaae02edc4a593b" id="r_ad8dfcc9b50d9bae82eaae02edc4a593b"><td class="memItemLeft" align="right" valign="top">RealMatrix const &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#ad8dfcc9b50d9bae82eaae02edc4a593b">mean</a> (<a class="el" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">StatisticsBatch</a> const &amp;statistics) const</td></tr>
<tr class="memdesc:ad8dfcc9b50d9bae82eaae02edc4a593b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Returns the mean given the state of the connected layer, i.e. in this case the mean of the Gaussian.  <br /></td></tr>
<tr class="separator:ad8dfcc9b50d9bae82eaae02edc4a593b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae920fde3c4ed485452214e90dabbadd4" id="r_ae920fde3c4ed485452214e90dabbadd4"><td class="memTemplParams" colspan="2">template&lt;class Matrix , class BetaVector &gt; </td></tr>
<tr class="memitem:ae920fde3c4ed485452214e90dabbadd4"><td class="memTemplItemLeft" align="right" valign="top">RealVector&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#ae920fde3c4ed485452214e90dabbadd4">energyTerm</a> (Matrix const &amp;state, BetaVector const &amp;beta) const</td></tr>
<tr class="memdesc:ae920fde3c4ed485452214e90dabbadd4"><td class="mdescLeft">&#160;</td><td class="mdescRight">The energy term this neuron adds to the energy function for a batch of inputs.  <br /></td></tr>
<tr class="separator:ae920fde3c4ed485452214e90dabbadd4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ada4d3177d4f9f4befe18168f9281ae47" id="r_ada4d3177d4f9f4befe18168f9281ae47"><td class="memTemplParams" colspan="2">template&lt;class Input &gt; </td></tr>
<tr class="memitem:ada4d3177d4f9f4befe18168f9281ae47"><td class="memTemplItemLeft" align="right" valign="top">double&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#ada4d3177d4f9f4befe18168f9281ae47">logMarginalize</a> (const Input &amp;inputs, double beta) const</td></tr>
<tr class="memdesc:ada4d3177d4f9f4befe18168f9281ae47"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sums over all possible values of the terms of the energy function which depend on the this layer and returns the logarithmic result.  <br /></td></tr>
<tr class="separator:ada4d3177d4f9f4befe18168f9281ae47"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a04bb5c1a6335d0d2b199804875c0ceb8" id="r_a04bb5c1a6335d0d2b199804875c0ceb8"><td class="memTemplParams" colspan="2">template&lt;class Vector , class SampleBatch , class Vector2 &gt; </td></tr>
<tr class="memitem:a04bb5c1a6335d0d2b199804875c0ceb8"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a04bb5c1a6335d0d2b199804875c0ceb8">expectedParameterDerivative</a> (Vector &amp;derivative, SampleBatch const &amp;samples, Vector2 const &amp;weights) const</td></tr>
<tr class="separator:a04bb5c1a6335d0d2b199804875c0ceb8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0fb9583b9a3316795085314c93499e08" id="r_a0fb9583b9a3316795085314c93499e08"><td class="memTemplParams" colspan="2">template&lt;class Vector , class SampleBatch , class WeightVector &gt; </td></tr>
<tr class="memitem:a0fb9583b9a3316795085314c93499e08"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a0fb9583b9a3316795085314c93499e08">parameterDerivative</a> (Vector &amp;derivative, SampleBatch const &amp;samples, WeightVector const &amp;weights) const</td></tr>
<tr class="memdesc:a0fb9583b9a3316795085314c93499e08"><td class="mdescLeft">&#160;</td><td class="mdescRight">Calculates the derivatives of the energy term of this neuron layer with respect to it's parameters - the bias weights.  <br /></td></tr>
<tr class="separator:a0fb9583b9a3316795085314c93499e08"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4dbab4818dff8ee8011b94713513bf4f" id="r_a4dbab4818dff8ee8011b94713513bf4f"><td class="memItemLeft" align="right" valign="top">RealVector&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a4dbab4818dff8ee8011b94713513bf4f">parameterVector</a> () const</td></tr>
<tr class="memdesc:a4dbab4818dff8ee8011b94713513bf4f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Returns the vector with the parameters associated with the neurons in the layer.  <br /></td></tr>
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<tr class="memitem:a9ec36fc2567fe4b55fb2b976baea9be4" id="r_a9ec36fc2567fe4b55fb2b976baea9be4"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a9ec36fc2567fe4b55fb2b976baea9be4">setParameterVector</a> (RealVector const &amp;newParameters)</td></tr>
<tr class="memdesc:a9ec36fc2567fe4b55fb2b976baea9be4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Returns the vector with the parameters associated with the neurons in the layer.  <br /></td></tr>
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<tr class="memitem:a4282e99234da5b7ee7de906abd2037cb" id="r_a4282e99234da5b7ee7de906abd2037cb"><td class="memItemLeft" align="right" valign="top">std::size_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a4282e99234da5b7ee7de906abd2037cb">numberOfParameters</a> () const</td></tr>
<tr class="memdesc:a4282e99234da5b7ee7de906abd2037cb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Returns the number of the parameters associated with the neurons in the layer.  <br /></td></tr>
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<tr class="memitem:a8c717e6c37de891dd44a1a4640d589a0" id="r_a8c717e6c37de891dd44a1a4640d589a0"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_gaussian_layer.html#a8c717e6c37de891dd44a1a4640d589a0">read</a> (<a class="el" href="namespaceshark.html#ada68729491840669e47c8ad42282424f">InArchive</a> &amp;archive)</td></tr>
<tr class="memdesc:a8c717e6c37de891dd44a1a4640d589a0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Reads the bias parameters from an archive.  <br /></td></tr>
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<tr class="memdesc:af6a8b46ec5a2bbef8c4fce3c17ddd565"><td class="mdescLeft">&#160;</td><td class="mdescRight">Writes the bias parameters to an archive.  <br /></td></tr>
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<tr class="inherit_header pub_methods_classshark_1_1_i_serializable"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classshark_1_1_i_serializable')"><img src="closed.png" alt="-"/>&#160;Public Member Functions inherited from <a class="el" href="classshark_1_1_i_serializable.html">shark::ISerializable</a></td></tr>
<tr class="memitem:a7baa9ce108d7278822297ce15882782a inherit pub_methods_classshark_1_1_i_serializable" id="r_a7baa9ce108d7278822297ce15882782a"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_i_serializable.html#a7baa9ce108d7278822297ce15882782a">~ISerializable</a> ()</td></tr>
<tr class="memdesc:a7baa9ce108d7278822297ce15882782a inherit pub_methods_classshark_1_1_i_serializable"><td class="mdescLeft">&#160;</td><td class="mdescRight">Virtual d'tor.  <br /></td></tr>
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<tr class="memitem:abdda0c5b8e065b8afbac2cba8f58e841 inherit pub_methods_classshark_1_1_i_serializable" id="r_abdda0c5b8e065b8afbac2cba8f58e841"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_i_serializable.html#abdda0c5b8e065b8afbac2cba8f58e841">load</a> (<a class="el" href="namespaceshark.html#ada68729491840669e47c8ad42282424f">InArchive</a> &amp;archive, unsigned int version)</td></tr>
<tr class="memdesc:abdda0c5b8e065b8afbac2cba8f58e841 inherit pub_methods_classshark_1_1_i_serializable"><td class="mdescLeft">&#160;</td><td class="mdescRight">Versioned loading of components, calls read(...).  <br /></td></tr>
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<tr class="memitem:a5bf66fa8db15cc529bec98976a2f5255 inherit pub_methods_classshark_1_1_i_serializable" id="r_a5bf66fa8db15cc529bec98976a2f5255"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_i_serializable.html#a5bf66fa8db15cc529bec98976a2f5255">save</a> (<a class="el" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524">OutArchive</a> &amp;archive, unsigned int version) const</td></tr>
<tr class="memdesc:a5bf66fa8db15cc529bec98976a2f5255 inherit pub_methods_classshark_1_1_i_serializable"><td class="mdescLeft">&#160;</td><td class="mdescRight">Versioned storing of components, calls write(...).  <br /></td></tr>
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<tr class="inherit_header pub_methods_classshark_1_1_i_parameterizable"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classshark_1_1_i_parameterizable')"><img src="closed.png" alt="-"/>&#160;Public Member Functions inherited from <a class="el" href="classshark_1_1_i_parameterizable.html">shark::IParameterizable&lt; VectorType &gt;</a></td></tr>
<tr class="memitem:a9e3a11172e74d1aa7292f3de4e2b6ebc inherit pub_methods_classshark_1_1_i_parameterizable" id="r_a9e3a11172e74d1aa7292f3de4e2b6ebc"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classshark_1_1_i_parameterizable.html#a9e3a11172e74d1aa7292f3de4e2b6ebc">~IParameterizable</a> ()</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>A layer of Gaussian neurons. </p>
<p>For a Gaussian neuron/variable the conditional probability distribution of the state of the variable given the state of the other layer is given by a Gaussian distribution with the input of the neuron as mean and unit variance. </p>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00048">48</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>
</div><h2 class="groupheader">Member Typedef Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#a346b4d4edf91fa583578207488fc7fca">&#9670;&#160;</a></span>StateSpace</h2>

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          <td class="memname">typedef <a class="el" href="structshark_1_1_real_space.html">RealSpace</a> <a class="el" href="classshark_1_1_gaussian_layer.html#a346b4d4edf91fa583578207488fc7fca">shark::GaussianLayer::StateSpace</a></td>
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<p>the bias terms associated with the neurons </p>
<p>the state space of this neuron is binary </p>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00053">53</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a510c15554b7b4bebec1c966e7ccaff95">&#9670;&#160;</a></span>StatisticsBatch</h2>

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          <td class="memname">typedef <a class="el" href="structshark_1_1_batch.html">Batch</a>&lt;<a class="el" href="classshark_1_1_gaussian_layer.html#a4c79a6809fa54a8788fc9a73f9661759">SufficientStatistics</a>&gt;::type <a class="el" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">shark::GaussianLayer::StatisticsBatch</a></td>
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<p>Sufficient statistics of a batch of data. </p>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00058">58</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a4c79a6809fa54a8788fc9a73f9661759">&#9670;&#160;</a></span>SufficientStatistics</h2>

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          <td class="memname">typedef RealVector <a class="el" href="classshark_1_1_gaussian_layer.html#a4c79a6809fa54a8788fc9a73f9661759">shark::GaussianLayer::SufficientStatistics</a></td>
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<p>The sufficient statistics for the Guassian Layer stores the mean of the neuron and the inverse temperature. </p>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00056">56</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

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<h2 class="groupheader">Member Function Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#aaf729abb5e4ab69322d57f2a1b5fe39b">&#9670;&#160;</a></span>bias() <span class="overload">[1/2]</span></h2>

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<p>Returns the bias values of the units. </p>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00065">65</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a877687dcf1eb19c17c0a2c941e7bf0a5">&#9670;&#160;</a></span>bias() <span class="overload">[2/2]</span></h2>

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<p>Returns the bias values of the units. </p>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00061">61</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#ae920fde3c4ed485452214e90dabbadd4">&#9670;&#160;</a></span>energyTerm()</h2>

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template&lt;class Matrix , class BetaVector &gt; </div>
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          <td class="paramname"><em>state</em>, </td>
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<p>The energy term this neuron adds to the energy function for a batch of inputs. </p>
<dl class="params"><dt>Parameters</dt><dd>
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    <tr><td class="paramname">state</td><td>the state of the neuron layer </td></tr>
    <tr><td class="paramname">beta</td><td>the inverse temperature of the i-th state </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>the energy term of the neuron layer </dd></dl>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00179">179</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

<p class="reference">References <a class="el" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">shark::batchSize()</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">size()</a>, and <a class="el" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a04bb5c1a6335d0d2b199804875c0ceb8">&#9670;&#160;</a></span>expectedParameterDerivative()</h2>

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template&lt;class Vector , class SampleBatch , class Vector2 &gt; </div>
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          <td class="paramname"><em>derivative</em>, </td>
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<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00222">222</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

<p class="reference">References <a class="el" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">size()</a>, and <a class="el" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a9d8d02a951be66e02b06e21c9b646ab8">&#9670;&#160;</a></span>expectedPhiValue()</h2>

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          <td class="paramtype"><a class="el" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">StatisticsBatch</a> const &amp;&#160;</td>
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<p>Returns the expectation of the phi-function. </p>
<dl class="params"><dt>Parameters</dt><dd>
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    <tr><td class="paramname">statistics</td><td>the sufficient statistics (the mean of the distribution). </td></tr>
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  </dd>
</dl>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00161">161</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

<p class="reference">References <a class="el" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">size()</a>, and <a class="el" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#ada4d3177d4f9f4befe18168f9281ae47">&#9670;&#160;</a></span>logMarginalize()</h2>

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<p>Sums over all possible values of the terms of the energy function which depend on the this layer and returns the logarithmic result. </p>
<p>This function is called by <a class="el" href="structshark_1_1_energy.html" title="The Energy function determining the Gibbs distribution of an RBM.">Energy</a> when the unnormalized marginal probability of the connected layer is to be computed. This function calculates the part which depends on the neurons which are to be marginalized out. (In the case of the binary hidden neuron, this is the term \(  \log \sum_h e^{\vec h^T W \vec v+ \vec h^T \vec c} \)). The rest is calculated by the energy function. In the general case of a hidden layer, this function calculates \( \log \int_h e^(\phi_h(\vec h)^T W \phi_v(\vec v)+f_h(\vec h) ) \) where f_h is the energy term of this.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">inputs</td><td>the inputs of the neurons they get from the other layer </td></tr>
    <tr><td class="paramname">beta</td><td>the inverse temperature of the <a class="el" href="classshark_1_1_r_b_m.html" title="stub for the RBM class. at the moment it is just a holder of the parameter set and the Energy.">RBM</a> </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>the marginal distribution of the connected layer </dd></dl>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00209">209</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

<p class="reference">References <a class="el" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">size()</a>, <a class="el" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>, <a class="el" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63">shark::sqr()</a>, and <a class="el" href="group__shark__globals.html#ga49b759f4712477bf89276a6c944fec47">shark::SQRT_2_PI</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a117e4b765db5d8b37c2446ae6451937e">&#9670;&#160;</a></span>logProbability()</h2>

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          <td class="paramtype"><a class="el" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">StatisticsBatch</a> const &amp;&#160;</td>
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          <td class="paramname"><em>state</em>&#160;</td>
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<p>Computes the log of the probability of the given states in the conditional distribution. </p>
<p>Currently it is only possible to compute the case with alpha=0</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">statistics</td><td>the statistics of the conditional distribution </td></tr>
    <tr><td class="paramname">state</td><td>the state to check </td></tr>
  </table>
  </dd>
</dl>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00132">132</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

<p class="reference">References <a class="el" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">size()</a>, <a class="el" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>, and <a class="el" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63">shark::sqr()</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#ad8dfcc9b50d9bae82eaae02edc4a593b">&#9670;&#160;</a></span>mean()</h2>

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          <td class="paramtype"><a class="el" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">StatisticsBatch</a> const &amp;&#160;</td>
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<p>Returns the mean given the state of the connected layer, i.e. in this case the mean of the Gaussian. </p>
<dl class="params"><dt>Parameters</dt><dd>
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    <tr><td class="paramname">statistics</td><td>the sufficient statistics of the layer for a whole batch </td></tr>
  </table>
  </dd>
</dl>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00168">168</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

<p class="reference">References <a class="el" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">size()</a>, and <a class="el" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a4282e99234da5b7ee7de906abd2037cb">&#9670;&#160;</a></span>numberOfParameters()</h2>

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<p>Returns the number of the parameters associated with the neurons in the layer. </p>

<p>Reimplemented from <a class="el" href="classshark_1_1_i_parameterizable.html#aed1e8d1d4dbde387e2f6a25141ed3a20">shark::IParameterizable&lt; VectorType &gt;</a>.</p>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00250">250</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

<p class="reference">References <a class="el" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">size()</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a0fb9583b9a3316795085314c93499e08">&#9670;&#160;</a></span>parameterDerivative()</h2>

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          <td class="paramname"><em>derivative</em>, </td>
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<p>Calculates the derivatives of the energy term of this neuron layer with respect to it's parameters - the bias weights. </p>
<p>This function takes a batch of samples and calculates a weighted derivative </p><dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">derivative</td><td>the derivative with respect to the parameters, the result is added on top of it to accumulate derivatives </td></tr>
    <tr><td class="paramname">samples</td><td>the sample from which the informations can be extracted </td></tr>
    <tr><td class="paramname">weights</td><td>the weights for the single sample derivatives </td></tr>
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<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00234">234</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

<p class="reference">References <a class="el" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">size()</a>, and <a class="el" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a4dbab4818dff8ee8011b94713513bf4f">&#9670;&#160;</a></span>parameterVector()</h2>

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<p>Returns the vector with the parameters associated with the neurons in the layer. </p>

<p>Reimplemented from <a class="el" href="classshark_1_1_i_parameterizable.html#afaa2ba692ab64a0edbff60d7ee6794db">shark::IParameterizable&lt; VectorType &gt;</a>.</p>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00240">240</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#ab7e9bdbf26ccea6190311b81ee4741cb">&#9670;&#160;</a></span>phi()</h2>

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<p>Transforms the current state of the neurons for the multiplication with the weight matrix of the <a class="el" href="classshark_1_1_r_b_m.html" title="stub for the RBM class. at the moment it is just a holder of the parameter set and the Energy.">RBM</a>, i.e. calculates the value of the phi-function used in the interaction term. In the case of Gaussian neurons the phi-function is just the identity. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">state</td><td>the state matrix of the neuron layer </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>the value of the phi-function </dd></dl>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00153">153</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

<p class="reference">References <a class="el" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">size()</a>, and <a class="el" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a8c717e6c37de891dd44a1a4640d589a0">&#9670;&#160;</a></span>read()</h2>

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          <td class="paramtype"><a class="el" href="namespaceshark.html#ada68729491840669e47c8ad42282424f">InArchive</a> &amp;&#160;</td>
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<p>Reads the bias parameters from an archive. </p>

<p>Reimplemented from <a class="el" href="classshark_1_1_i_serializable.html#ad4ad9a7c274deff642f91e98417fbc63">shark::ISerializable</a>.</p>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00255">255</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#ad0d61deb12bd4455dbf6546d08328b72">&#9670;&#160;</a></span>resize()</h2>

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<p>Resizes this neuron layer. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">newSize</td><td>number of neurons in the layer </td></tr>
  </table>
  </dd>
</dl>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00072">72</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a11e7d48c8d1b8faf811d3870d9ce818a">&#9670;&#160;</a></span>sample()</h2>

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          <td class="paramtype"><a class="el" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">StatisticsBatch</a> const &amp;&#160;</td>
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          <td class="paramname"><em>rng</em>&#160;</td>
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<p>Given a the precomputed statistics (the mean of the Gaussian), the elements of the vector are sampled. This happens either with Gibbs-Sampling or Flip-the-State sampling. For alpha= 0 gibbs sampling is performed. That is the next state for neuron i is directly taken from the conditional distribution of the i-th neuron. In the case of alpha=1, flip-the-state sampling is performed, which takes the last state into account and tries to do deterministically jump into states with higher probability. THIS IS NOT IMPLEMENTED YET and alpha is ignored! </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">statistics</td><td>sufficient statistics containing the mean of the conditional Gaussian distribution of the neurons </td></tr>
    <tr><td class="paramname">state</td><td>the state matrix that will hold the sampled states </td></tr>
    <tr><td class="paramname">alpha</td><td>factor changing from gibbs to flip-the state sampling. 0&lt;=alpha&lt;=1 </td></tr>
    <tr><td class="paramname">rng</td><td>the random number generator used for sampling </td></tr>
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<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00110">110</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

<p class="reference">References <a class="el" href="namespaceshark_1_1random.html#a972c5f7f031612a130aa077fc9136a9f">shark::random::gauss()</a>, <a class="el" href="_open_m_p_8h.html#a6de33df9d72bea69f903cffb391e7121">SHARK_CRITICAL_REGION</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">size()</a>, and <a class="el" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a9ec36fc2567fe4b55fb2b976baea9be4">&#9670;&#160;</a></span>setParameterVector()</h2>

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<p>Returns the vector with the parameters associated with the neurons in the layer. </p>

<p>Reimplemented from <a class="el" href="classshark_1_1_i_parameterizable.html#ad5e35d1a10ff36fa72ea787baa40e9ad">shark::IParameterizable&lt; VectorType &gt;</a>.</p>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00245">245</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#ad2a3749d604f6e425f069726cae281d1">&#9670;&#160;</a></span>size()</h2>

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<p>Returns the number of neurons of this layer. </p>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00077">77</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

<p class="reference">Referenced by <a class="el" href="classshark_1_1_gaussian_layer.html#ae920fde3c4ed485452214e90dabbadd4">energyTerm()</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#a04bb5c1a6335d0d2b199804875c0ceb8">expectedParameterDerivative()</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#a9d8d02a951be66e02b06e21c9b646ab8">expectedPhiValue()</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#ada4d3177d4f9f4befe18168f9281ae47">logMarginalize()</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#a117e4b765db5d8b37c2446ae6451937e">logProbability()</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#ad8dfcc9b50d9bae82eaae02edc4a593b">mean()</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#a4282e99234da5b7ee7de906abd2037cb">numberOfParameters()</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#a0fb9583b9a3316795085314c93499e08">parameterDerivative()</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#ab7e9bdbf26ccea6190311b81ee4741cb">phi()</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#a11e7d48c8d1b8faf811d3870d9ce818a">sample()</a>, and <a class="el" href="classshark_1_1_gaussian_layer.html#a0e768ac30ba61b25220fa17293591d0c">sufficientStatistics()</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#a0e768ac30ba61b25220fa17293591d0c">&#9670;&#160;</a></span>sufficientStatistics()</h2>

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template&lt;class Input , class BetaVector &gt; </div>
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          <td class="memname">void shark::GaussianLayer::sufficientStatistics </td>
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          <td class="paramtype"><a class="el" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">StatisticsBatch</a> &amp;&#160;</td>
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<p>Takes the input of the neuron and estimates the expectation of the response of the neuron. </p>
<dl class="params"><dt>Parameters</dt><dd>
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    <tr><td class="paramname">input</td><td>the batch of inputs of the neuron </td></tr>
    <tr><td class="paramname">statistics</td><td>sufficient statistics containing the mean of the resulting Gaussian distribution </td></tr>
    <tr><td class="paramname">beta</td><td>the inverse Temperature of the <a class="el" href="classshark_1_1_r_b_m.html" title="stub for the RBM class. at the moment it is just a holder of the parameter set and the Energy.">RBM</a> (typically 1) for the whole batch </td></tr>
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<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00087">87</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

<p class="reference">References <a class="el" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">size()</a>, and <a class="el" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>.</p>

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<h2 class="memtitle"><span class="permalink"><a href="#af6a8b46ec5a2bbef8c4fce3c17ddd565">&#9670;&#160;</a></span>write()</h2>

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          <td class="memname">void shark::GaussianLayer::write </td>
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          <td class="paramtype"><a class="el" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524">OutArchive</a> &amp;&#160;</td>
          <td class="paramname"><em>archive</em></td><td>)</td>
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<p>Writes the bias parameters to an archive. </p>

<p>Reimplemented from <a class="el" href="classshark_1_1_i_serializable.html#a9bddedd42933c922e323b73131f62f12">shark::ISerializable</a>.</p>

<p class="definition">Definition at line <a class="el" href="_gaussian_layer_8h_source.html#l00259">259</a> of file <a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a>.</p>

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<hr/>The documentation for this class was generated from the following file:<ul>
<li>include/shark/Unsupervised/RBM/Neuronlayers/<a class="el" href="_gaussian_layer_8h_source.html">GaussianLayer.h</a></li>
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